Imperial College London

DrErikVolz

Faculty of MedicineSchool of Public Health

Senior Lecturer
 
 
 
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Contact

 

+44 (0)20 7594 1933e.volz Website

 
 
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Location

 

UG10Norfolk PlaceSt Mary's Campus

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Summary

 

Publications

Publication Type
Year
to

59 results found

Volz EM, Koelle K, Bedford T, 2013, Viral Phylodynamics, PLOS COMPUTATIONAL BIOLOGY, Vol: 9

Journal article

Alam SJ, Zhang X, Romero-Severson EO, Henry C, Zhong L, Volz EM, Brenner BG, Koopman JSet al., 2013, Detectable signals of episodic risk effects on acute HIV transmission: Strategies for analyzing transmission systems using genetic data, EPIDEMICS, Vol: 5, Pages: 44-55, ISSN: 1755-4365

Journal article

Sadasivam RS, Cutrona SL, Volz E, Rao SR, Houston TKet al., 2013, Web-based Peer-Driven Chain Referrals for Smoking Cessation, MEDINFO 2013: PROCEEDINGS OF THE 14TH WORLD CONGRESS ON MEDICAL AND HEALTH INFORMATICS, PTS 1 AND 2, Vol: 192, Pages: 357-361, ISSN: 0926-9630

Journal article

Miller JC, Volz EM, 2013, Incorporating Disease and Population Structure into Models of SIR Disease in Contact Networks, PloS one, Vol: 8, Pages: e69162-e69162

Journal article

Romero-Severson EO, Alam SJ, Volz EM, Koopman JSet al., 2012, Heterogeneity in Number and Type of Sexual Contacts in a Gay Urban Cohort., Stat Commun Infect Dis, Vol: 4, ISSN: 1948-4690

HIV transmission models include heterogeneous individuals with different sexual behaviors including contact rates, mixing patterns, and sexual practices. However, heterogeneity can also exist within individuals over time. In this paper we analyze a two year prospective cohort of 882 gay men with observations at six month intervals focusing on heterogeneity both within and between individuals in sexual contact rates and sexual roles. The total number of sexual contacts made over the course of the study (mean 1.55 per month) are highly variable between individuals (standard deviation 9.82 per month) as expected. At the individual level, contacts were also heterogeneous over time. For a homogeneous count process the variance should scale with the mean; however, at the individual level the variance scaled with the square root of the mean implying the presence of heterogeneity within individuals over time. We also observed a high level of movement between dichotomous sexual roles (insertive/receptive, protected/unprotected, anal/oral, and HIV status of partners). On average periods of exclusively unprotected sexual contacted lasted 16 months. Our results suggest that future HIV models should consider heterogeneities both between and within individuals in sexual contact rates and sexual roles.

Journal article

Zhang X, Zhong L, Romero-Severson E, Alam SJ, Henry CJ, Volz EM, Koopman JSet al., 2012, Episodic HIV Risk Behavior Can Greatly Amplify HIV Prevalence and the Fraction of Transmissions from Acute HIV Infection., Stat Commun Infect Dis, Vol: 4, ISSN: 1948-4690

A deterministic compartmental model was explored that relaxed the unrealistic assumption in most HIV transmission models that behaviors of individuals are constant over time. A simple model was formulated to better explain the effects observed. Individuals had a high and a low contact rate and went back and forth between them. This episodic risk behavior interacted with the short period of high transmissibility during acute HIV infection to cause dramatic increases in prevalence as the differences between high and low contact rates increased and as the duration of high risk better matched the duration of acute HIV infection. These same changes caused a considerable increase in the fraction of all transmissions that occurred during acute infection. These strong changes occurred despite a constant total number of contacts and a constant total transmission potential from acute infection. Two phenomena played a strong role in generating these effects. First, people were infected more often during their high contact rate phase and they remained with high contact rates during the highly contagious acute infection stage. Second, when individuals with previously low contact rates moved into an episodic high-risk period, they were more likely to be susceptible and thus provided more high contact rate susceptible individuals who could get infected. These phenomena make test and treat control strategies less effective and could cause some behavioral interventions to increase transmission. Signature effects on genetic patterns between HIV strains could make it possible to determine whether these episodic risk effects are acting in a population.

Journal article

Bauermeister JA, Zimmerman MA, Johns MM, Glowacki P, Stoddard S, Volz Eet al., 2012, Innovative Recruitment Using Online Networks: Lessons Learned From an Online Study of Alcohol and Other Drug Use Utilizing a Web-Based, Respondent-Driven Sampling (webRDS) Strategy, JOURNAL OF STUDIES ON ALCOHOL AND DRUGS, Vol: 73, Pages: 834-838, ISSN: 1937-1888

Journal article

Volz EM, Koopman JS, Ward MJ, Brown AL, Frost SDWet al., 2012, Simple Epidemiological Dynamics Explain Phylogenetic Clustering of HIV from Patients with Recent Infection, PLOS COMPUTATIONAL BIOLOGY, Vol: 8

Journal article

Miller JC, Slim AC, Volz EM, 2012, Edge-based compartmental modelling for infectious disease spread, JOURNAL OF THE ROYAL SOCIETY INTERFACE, Vol: 9, Pages: 890-906, ISSN: 1742-5689

Journal article

Volz EM, 2012, Complex Population Dynamics and the Coalescent Under Neutrality, GENETICS, Vol: 190, Pages: 187-U311, ISSN: 0016-6731

Journal article

Volz EM, Miller JC, Galvani A, Ancel Meyers Let al., 2011, Correction: Effects of Heterogeneous and Clustered Contact Patterns on Infectious Disease Dynamics, PLoS Computational Biology, Vol: 7

Journal article

Craft ME, Volz E, Packer C, Meyers LAet al., 2011, Disease transmission in territorial populations: the small-world network of Serengeti lions, JOURNAL OF THE ROYAL SOCIETY INTERFACE, Vol: 8, Pages: 776-786, ISSN: 1742-5689

Journal article

Volz EM, Miller JC, Galvani A, Meyers LAet al., 2011, Effects of Heterogeneous and Clustered Contact Patterns on Infectious Disease Dynamics, PLOS COMPUTATIONAL BIOLOGY, Vol: 7

Journal article

Volz E, Frost SDW, Rothenberg R, Meyers LAet al., 2010, Epidemiological bridging by injection drug use drives an early HIV epidemic, EPIDEMICS, Vol: 2, Pages: 155-164, ISSN: 1755-4365

Journal article

Frost SDW, Volz EM, 2010, Viral phylodynamics and the search for an 'effective number of infections', PHILOSOPHICAL TRANSACTIONS OF THE ROYAL SOCIETY B-BIOLOGICAL SCIENCES, Vol: 365, Pages: 1879-1890, ISSN: 0962-8436

Journal article

Farber S, Páez A, Volz E, 2010, Topology, dependency tests and estimation bias in network autoregressive models, Advances in Spatial Science, Pages: 29-57

© 2010, Springer-Verlag Berlin Heidelberg. Regression analyses based on spatial datasets often display spatial autocorrelation in the substantive part of the model, or residual pattern in the disturbances. A researcher conducting investigations of a spatial dataset must be able to identify whether this is the case, and if so, what model specification is more appropriate for the data and problem at hand. If autocorrelation is embedded in the dependent variable, the following spatial autoregressive (SAR) model with a spatial lag can be used: (Formula Presented.) On the other hand, when there is residual pattern in the error component of the traditional regression model, the spatial error model (SEM) can be used: (Formula Presented.) In the above equations, W is the spatial weight matrix representing the structure of the spatial relationships between observations, ρ is the spatial dependence parameter, u is a vector of autocorrelated disturbances, and all other terms are the elements commonly found in ordinary linear regression analysis.

Book chapter

Volz EM, Pond SLK, Ward MJ, Brown AJL, Frost SDWet al., 2009, Phylodynamics of Infectious Disease Epidemics, GENETICS, Vol: 183, Pages: 1421-1430, ISSN: 0016-6731

Journal article

Craft ME, Volz E, Packer C, Meyers LAet al., 2009, Distinguishing epidemic waves from disease spillover in a wildlife population, PROCEEDINGS OF THE ROYAL SOCIETY B-BIOLOGICAL SCIENCES, Vol: 276, Pages: 1777-1785, ISSN: 0962-8452

Journal article

Farber S, Paez A, Volz E, 2009, Topology and Dependency Tests in Spatial and Network Autoregressive Models, GEOGRAPHICAL ANALYSIS, Vol: 41, Pages: 158-180, ISSN: 0016-7363

Journal article

Volz E, Meyers LA, 2009, Epidemic thresholds in dynamic contact networks, JOURNAL OF THE ROYAL SOCIETY INTERFACE, Vol: 6, Pages: 233-241, ISSN: 1742-5689

Journal article

Abramovitz D, Volz EM, Strathdee SA, Patterson TL, Vera A, Frost SDWet al., 2009, Using Respondent Driven Sampling in a Hidden Population at Risk of HIV Infection: Who do HIV-positive recruiters recruit?, Sexually transmitted diseases, Vol: 36, Pages: 750-750

Journal article

Paez A, Scott DM, Volz E, 2008, A discrete-choice approach to modeling social influence on individual decision making, ENVIRONMENT AND PLANNING B-PLANNING & DESIGN, Vol: 35, Pages: 1055-1069, ISSN: 0265-8135

Journal article

Paez A, Scott DM, Volz E, 2008, Weight matrices for social influence analysis: An investigation of measurement errors and their effect on model identification and estimation quality, SOCIAL NETWORKS, Vol: 30, Pages: 309-317, ISSN: 0378-8733

Journal article

Volz E, 2008, Susceptible-infected-recovered epidemics in populations with heterogeneous contact rates, EUROPEAN PHYSICAL JOURNAL B, Vol: 63, Pages: 381-386, ISSN: 1434-6028

Journal article

Volz E, 2008, SIR dynamics in random networks with heterogeneous connectivity, JOURNAL OF MATHEMATICAL BIOLOGY, Vol: 56, Pages: 293-310, ISSN: 0303-6812

Journal article

Volz E, Heckathorn DD, 2008, Probability Based Estimation Theory for Respondent Driven Sampling, JOURNAL OF OFFICIAL STATISTICS, Vol: 24, Pages: 79-97, ISSN: 0282-423X

Journal article

Volz E, Meyers LA, 2007, Susceptible-infected-recovered epidemics in dynamic contact networks, PROCEEDINGS OF THE ROYAL SOCIETY B-BIOLOGICAL SCIENCES, Vol: 274, Pages: 2925-2933, ISSN: 0962-8452

Journal article

Volz E, 2004, Random networks with tunable degree distribution and clustering, Physical Review E, Vol: 70, Pages: 056115-056115

Journal article

Volz EM, Siveroni I, Bayesian phylodynamic inference with complex models

<jats:title>Abstract</jats:title><jats:p>Population genetic modeling can enhance Bayesian phylogenetic inference by providing a realistic prior on the distribution of branch lengths and times of common ancestry.The parameters of a population genetic model may also have intrinsic importance, and simultaneous estimation of a phylogeny and model parameters has enabled phylodynamic inference of population growth rates, reproduction numbers, and effective population size through time. Phylodynamic inference based on pathogen genetic sequence data has emerged as useful supplement to epidemic surveillance, however commonly-used mechanistic models that are typically fitted to non-genetic surveillance data are rarely fitted to pathogen genetic data due to a dearth of software tools, and the theory required to conduct such inference has been developed only recently. We present a framework for coalescent-based phylogenetic and phylodynamic inference which enables highly-flexible modeling of demographic and epidemiological processes. This approach builds upon previous structured coalescent approaches and includes enhancements for computational speed, accuracy, and stability. A flexible markup language is described for translating parametric demographic or epidemiological models into a structured coalescent model enabling simultaneous estimation of demographic or epidemiological parameters and time-scaled phylogenies. We demonstrate the utility of these approaches by fitting compartmental epidemiological models to Ebola virus and Influenza A virus sequence data, demonstrating how important features of these epidemics, such as the reproduction number and epidemic curves, can be gleaned from genetic data. These approaches are provided as an open-source package <jats:italic>PhyDyn</jats:italic> for the BEAST phylogenetics platform.</jats:p>

Journal article

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